Cross-Frequency Collaborative Low-Light Face Enhancement Guided by Structural Priors

ZHAO Ya; JIANG Liuyang; JIA Di; YAO Wenda

doi:10.11999/JEIT260843

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ZHAO Ya, JIANG Liuyang, JIA Di, YAO Wenda. Cross-Frequency Collaborative Low-Light Face Enhancement Guided by Structural Priors[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260843

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ZHAO Ya, JIANG Liuyang, JIA Di, YAO Wenda. Cross-Frequency Collaborative Low-Light Face Enhancement Guided by Structural Priors[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260843

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Cross-Frequency Collaborative Low-Light Face Enhancement Guided by Structural Priors

doi: 10.11999/JEIT260843 cstr: 32379.14.JEIT260843

ZHAO Ya^{1, 2
,
,},
JIANG Liuyang¹,
JIA Di¹,
YAO Wenda¹

1.
School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China
2.
Heilongjiang Key Laboratory of Big Data and Intelligent Analysis of Petroleum, Daqing 163318, China

Funds: National Natural Science Foundation of China (Grant No. 62471124); Natural Science Foundation of Heilongjiang Province (Grant No. LH2022F006); Key Project of Heilongjiang Provincial Education Science Planning (Grant No. GJB1421114)

Accepted Date: 2026-09-17
Rev Recd Date: 2026-09-17

Available Online: 2026-09-27

Abstract

Abstract

Objective Low-light face images commonly suffer from insufficient illumination, reduced contrast, amplified noise, and loss of local details. These degradations reduce visual quality and destabilize identity recognition, fatigue analysis, and face behavior understanding. Structural and discriminative information around the eyes and mouth is especially vulnerable to edge blurring, texture loss, and local structural shifts. Existing low-light image enhancement methods mainly focus on illumination recovery or general image reconstruction, while preservation of key face structures and identity-related representations remains limited. From a frequency-domain perspective, low-light degradation weakens low-frequency structural responses while disturbing high-frequency edges and textures, and directly enhancing high-frequency components without structural guidance may amplify noise or introduce false textures. Therefore, low-frequency face structures should guide spatially corresponding high-frequency detail restoration while maintaining structural stability during cross-frequency interaction and multi-scale reconstruction. To address these problems, a structural-prior-guided cross-frequency collaborative method is proposed for low-light face image enhancement. Methods An encoder-decoder enhancement network is constructed with structural-prior-guided cross-frequency association (CFA), high-frequency modeling (HFM), and face structural consistency modulation (FSCM) (Fig. 1). In CFA, input features are decomposed into low-frequency and directional high-frequency subbands using a discrete wavelet transform. Key-region heatmaps generated from facial landmarks are combined with directional structural responses and local statistical information to construct a spatial structural prior, which guides the restoration of spatially corresponding high-frequency details according to key-region locations, structural directions, and local significance, thereby reducing excessive enhancement of weak-structure or noise-dominated regions. HFM combines depthwise separable convolution with two-dimensional selective scanning. Local convolution is used to extract edge and texture details, whereas multi-directional selective scanning models long-range spatial dependencies. FSCM further regulates structurally relevant features during cross-frequency interaction and cross-scale feature fusion (Fig. 2). Its cross-frequency branch selectively modulates high-frequency responses using low-frequency structural statistics and key-region priors, while its cross-scale branch controls the transfer of shallow structural features through encoder-decoder skip connections. The network is jointly optimized using basic enhancement, identity consistency, key-region structure, and cross-frequency feature consistency constraints. CelebA is used for main training, and LaPa is used for pose adaptation and evaluation. Identity-level partitioning is adopted to avoid identity leakage, with 90%, 5%, and 5% of identities used for training, validation, and testing, respectively. Synthetic low-light images are generated by brightness attenuation, Gamma mapping, illumination-related noise, chromatic noise, color shifts, and black-level offsets. Dark Face is used only for real low-light evaluation. Results and Discussions The proposed model contains 1.47 M parameters and requires 11.74 G floating-point operations for a 256 × 256 input, with an average inference time of 70.48 ms and a speed of approximately 14.19 FPS. On CelebA-Test, the proposed method achieves 25.36 dB PSNR, 0.88 SSIM, 0.8934 ArcFace similarity, 0.0566 Eye-LPIPS, and 0.0753 Mouth-LPIPS, obtaining the best results among the compared methods on all five metrics (Table 1). On LaPa-Test, ΔEAR, Eye-LPIPS, ΔMAR, and Mouth-LPIPS reach 0.0065, 0.0512, 0.1219, and 0.0714, respectively, achieving the best results for all four key-region metrics (Table 2). The ArcFace similarity is 0.9398, lower than those of Low-FaceNet and SCI. Sample-wise analysis shows that this gap is weakly related to pose magnitude and remains in many samples with improved local metrics, indicating partial decoupling between key-region structural fidelity and whole-face identity embedding fidelity. Visual comparisons show improved illumination and better preservation of eye contours, mouth boundaries, and facial textures without obvious local over-enhancement (Fig. 3). On Dark Face, the proposed method obtains a NIQE of 10.92±1.12 and improves face-region visibility in representative samples, while challenging cases reveal limitations under nonuniform illumination, occlusion, and extremely low signal-to-noise ratios (Table 3, Fig. 4). Ablation experiments confirm the complementary effects of the three additional constraints and the joint contribution of CFA, HFM, and FSCM, with the complete model achieving the best overall results (Table 4). Landmark perturbation experiments further show that the continuous Gaussian heatmaps and structural modulation remain stable under spatial deviations of up to ±6 pixels (Table 5). Conclusions A structural-prior-guided cross-frequency collaborative method is proposed for low-light face image enhancement. Low-frequency face structures guide high-frequency detail restoration, while local convolution and two-dimensional selective scanning model fine details and long-range dependencies. FSCM further regulates structural feature transfer across frequencies and scales. Experiments on CelebA and LaPa demonstrate advantages in reconstruction quality, key-region structure preservation, and local perceptual consistency. LaPa results also show that local structural improvements do not always yield equivalent gains in whole-face identity embeddings, indicating room for further optimization of global identity consistency. Real low-light and landmark perturbation experiments indicate a certain degree of scene adaptability and robustness to small spatial deviations. However, performance remains limited under severe occlusion, small-scale faces, extreme low-light conditions, and mixed real degradations. Future work will investigate real low-light face data, denser and confidence-aware regional priors, adaptive constraints for different face regions, joint optimization of local structures and global identity representations, and collaborative optimization with downstream facial analysis tasks.
- Low-light face image enhancement,
- Cross-frequency collaborative modeling,
- Structural prior,
- Key-region structure preservation,
- Identity consistency

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References(25)

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